Typically, the training objectives for instance segmentation models consist of two major terms: object detection loss and instance mask loss.
In methods like Mask RCNN [1]}, the object detection loss is composed of a region proposal classification loss and a box regression loss, which are used to train both the region proposal network (RPN) and the region of interest (ROI) heads.
For simplicity, we summarize the objectives for RPN and ROI as one loss.
The instance mask loss trains the mask head using only positive regions, , IoU with the nearest ground truth \(>\)  0.5 by default in Mask RCNN.
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